{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rethinking the population pyramid\n",
    "\n",
    "This notebook provides the methodology and code used in the blog post, [Rethinking the population pyramid](http://www.randalolson.com/2015/07/14/rethinking-the-population-pyramid/).\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com/)\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is widely useable and shareable as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age_Range</th>\n",
       "      <th>Total_Pop_2010</th>\n",
       "      <th>Males_2010</th>\n",
       "      <th>Females_2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;5</td>\n",
       "      <td>20201362</td>\n",
       "      <td>10319427</td>\n",
       "      <td>9881935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5-9</td>\n",
       "      <td>20348657</td>\n",
       "      <td>10389638</td>\n",
       "      <td>9959019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10-14</td>\n",
       "      <td>20677194</td>\n",
       "      <td>10579862</td>\n",
       "      <td>10097332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15-19</td>\n",
       "      <td>22040343</td>\n",
       "      <td>11303666</td>\n",
       "      <td>10736677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20-24</td>\n",
       "      <td>21585999</td>\n",
       "      <td>11014176</td>\n",
       "      <td>10571823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age_Range  Total_Pop_2010  Males_2010  Females_2010\n",
       "0        <5        20201362    10319427       9881935\n",
       "1       5-9        20348657    10389638       9959019\n",
       "2     10-14        20677194    10579862      10097332\n",
       "3     15-19        22040343    11303666      10736677\n",
       "4     20-24        21585999    11014176      10571823"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# This is a custom matplotlib style that I use for most of my charts\n",
    "plt.style.use('https://gist.githubusercontent.com/rhiever/d0a7332fe0beebfdc3d5/raw/205e477cf231330fe2f265070f7c37982fd3130c/tableau10.mplstyle')\n",
    "\n",
    "age_gender_data = pd.read_csv('http://www.randalolson.com/wp-content/uploads/us-age-gender-breakdown.csv')\n",
    "age_gender_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Problems with the population pyramid\n",
    "\n",
    "###1) Violates the [standard expectation](http://mathbench.umd.edu/modules/visualization_graph/page02.htm) of having the causal variable on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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miDNnzuT000/n0EMP5ZxzzqF3796UlZWxePFiVq9ezUsvvZTpzg6hIDcv7IQW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\naYbKysqUUkplZGSoTZs2haQhMTGxwW9vLS8vVx06dFAHDx5USin1+uu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44QfExsbWG8P7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\nEV3FBSIREREREREBAP4fFr3zea97x3YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109fd0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 7))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "for (i, row) in age_gender_data.iterrows():\n",
    "    plt.bar([i, i], [row['Females_2010'], -row['Males_2010']],\n",
    "            color=['#CC6699', '#008AB8'], width=0.8, align='center', edgecolor='none')\n",
    "    \n",
    "plt.xlim(-0.6, 20.6)\n",
    "plt.ylim(-12.1e6, 12.1e6)\n",
    "#plt.grid(False, axis='x')\n",
    "plt.xticks(np.arange(0, 21), age_gender_data['Age_Range'], fontsize=11)\n",
    "plt.yticks(np.arange(-12e6, 13e6, 2e6),\n",
    "           ['{}m'.format(int(abs(x) / 1e6)) if x != 0 else 0 for x in np.arange(-12e6, 13e6, 2e6)])\n",
    "plt.xlabel('Age group')\n",
    "plt.ylabel('Number of people (millions)')\n",
    "\n",
    "plt.savefig('pop_pyramid_rotated.pdf')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###2) Doesn't allow direct comparisons between the two categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AQCsX8E1qKisr5XQ6lZCQoHPOOScYmST90Nz9+c9/1uOPP+61rWPHjh6PLRaL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b26cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 7))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "for (i, row) in age_gender_data.iterrows():\n",
    "    plt.bar([i - 0.2, i + 0.2], [row['Females_2010'], row['Males_2010']],\n",
    "            color=['#CC6699', '#008AB8'], width=0.4, align='center', edgecolor='none')\n",
    "    \n",
    "plt.xlim(-0.6, 20.6)\n",
    "plt.xticks(range(0, 21), age_gender_data['Age_Range'], fontsize=11)\n",
    "plt.grid(False, axis='x')\n",
    "plt.yticks(np.arange(0, 13e6, 1e6),\n",
    "           ['{}m'.format(int(x / 1e6)) if x > 0 else 0 for x in np.arange(0, 13e6, 1e6)])\n",
    "plt.xlabel('Age group')\n",
    "plt.ylabel('Number of people (millions)')\n",
    "\n",
    "plt.savefig('pop_pyramid_grouped.pdf')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###3) Relative trends between the categories can be masked by displaying absolute values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6dNCyZcs8FhAAAAAAYA9GZyAXLVqksWPHqmfPnrpw4UK+fREREUpNTfVENgAA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\nknT27FkNGTJEGzduVGpqqla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Bx/7222+Wj4/PNU74f3bu3GlFRkZa7733ntW4cWOrcePG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d38b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_gender_data['Male_Pct'] = age_gender_data['Males_2010'] / age_gender_data['Total_Pop_2010']\n",
    "age_gender_data['Female_Pct'] = age_gender_data['Females_2010'] / age_gender_data['Total_Pop_2010']\n",
    "\n",
    "plt.figure(figsize=(15, 7))\n",
    "\n",
    "for (i, row) in age_gender_data.iterrows():\n",
    "    plt.bar([i], [row['Male_Pct']],\n",
    "            color=['#008AB8'], width=0.9, align='center', edgecolor='none')\n",
    "    plt.bar([i], [row['Female_Pct']], bottom=[row['Male_Pct']],\n",
    "            color=['#CC6699'], width=0.9, align='center', edgecolor='none')\n",
    "    \n",
    "plt.xlim(-0.6, 20.6)\n",
    "plt.ylim(0, 1)\n",
    "plt.xticks(range(0, 21), age_gender_data['Age_Range'], fontsize=11)\n",
    "plt.grid(False)\n",
    "plt.yticks(np.arange(0, 1.01, 0.25),\n",
    "           ['{}%'.format(int(x * 100)) for x in np.arange(0, 1.01, 0.25)])\n",
    "plt.xlabel('Age group')\n",
    "plt.ylabel('Percentage of age group')\n",
    "\n",
    "plt.plot([-0.425, 20.425], [0.5, 0.5], lw=2, color='black')\n",
    "plt.plot([-0.425, 20.425], [0.25, 0.25], lw=2, color='black')\n",
    "\n",
    "plt.savefig('pop_pyramid_stacked.pdf')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
